---
license: other
license_name: lfm1.0
license_link: https://www.liquid.ai/license
base_model: LiquidAI/LFM2.5-350M-Base
datasets:
  - juanquivilla/sotto-transcript-cleanup
tags:
  - speech-to-text
  - transcript-cleanup
  - disfluency-correction
  - sotto-asr
  - lfm2
  - liquid-ai
  - text2text-generation
library_name: transformers
pipeline_tag: text-generation
language:
  - en
---

# SottoASR Transcript Cleanup — LFM2.5-350M (bf16)

<p align="center">
  <a href="https://sotto.app">sotto.app</a> · 
  <a href="https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit">MLX 5-bit (recommended for deployment)</a> · 
  <a href="https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m-mlx-4bit">MLX 4-bit</a> · 
  <a href="https://huggingface.co/datasets/juanquivilla/sotto-transcript-cleanup">Training Dataset</a>
</p>

## Overview

This is the **full-precision (bf16) fine-tuned** [LiquidAI/LFM2.5-350M-Base](https://huggingface.co/LiquidAI/LFM2.5-350M-Base) model for cleaning speech-to-text transcripts. It is the fine-tuned SLM (Small Language Model) powering on-device transcript cleanup in [**SottoASR**](https://sotto.app) — a local, privacy-first speech-to-text application for macOS.

**For on-device deployment, use the [MLX 5-bit quantized version](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit) (233MB, <0.5% quality loss).**

## What It Does

Takes raw, unpunctuated ASR output and produces clean, properly formatted text:

| Input (raw ASR) | Output (cleaned) |
|---|---|
| `uh the server is uh running low on memory` | The server is running low on memory. |
| `use redis wait no memcached is better` | Use Memcached. |
| `so basically the the api is um throttling our requests` | The API is throttling our requests. |
| `lets go ahead and really focus on the performance issue` | Let's go ahead and really focus on the performance issue. |
| `send the email to john period` | Send the email to John. |
| `me and the team is working on fixing it` | The team and I are working on fixing it. |

Handles: filler removal, crutch word removal, self-corrections, false starts, grammar fixes, misheard word correction, dictation commands (period→., comma→,, slash→/), list formatting, and wording preservation.

## Performance

| Metric | This Model (350M) | Prompted Qwen3.5-2B | Improvement |
|--------|-------------------|---------------------|-------------|
| **ROUGE-L** | **0.931** | 0.891 | **+4.5%** |
| **Exact Match** | **56%** | 37% | **+51% relative** |
| **Self-Correction** | **0.869** | 0.742 | **+17.1%** |
| **Zero-Filler Rate** | **90%** | 82% | **+9.8% relative** |
| **Inference** | **0.12s** | 1.0s | **8.3x faster** |
| **Model Size** | **354M params** | 2B params | **5.7x smaller** |

### Per-Category Scores

| Category | ROUGE-L | Description |
|----------|---------|-------------|
| preserve_wording | 0.987 | Clean input passes through unchanged |
| list_formatting | 0.972 | Spoken lists → numbered format |
| filler_removal | 0.955 | uh, um, uhm, er, ah |
| short | 0.940 | Brief utterances (2-10 words) |
| false_start | 0.926 | Stutters and restarts |
| dictation_commands | 0.971 | period→., comma→,, slash→/ |
| mixed | 0.928 | Multiple overlapping disfluencies |
| long_dictation | 0.918 | 100+ word passages |
| misheard_words | 0.913 | ASR errors (post gress→Postgres) |
| grammar | 0.906 | gonna→going to, me and him→he and I |
| crutch_words | 0.892 | basically, you know, I mean |
| self_correction | 0.869 | Speaker changes mind mid-sentence |

## Training

- **Base model:** [LiquidAI/LFM2.5-350M-Base](https://huggingface.co/LiquidAI/LFM2.5-350M-Base) (hybrid convolution + attention, 32K context)
- **Dataset:** [juanquivilla/sotto-transcript-cleanup](https://huggingface.co/datasets/juanquivilla/sotto-transcript-cleanup) — 124K synthetic pairs
- **Method:** Two-stage full fine-tuning
  1. **Stage 1:** Full FT on 124K dataset (LR 1e-5, 3 epochs, ~22 min on RTX 4090)
  2. **Stage 2:** Concentrated hard-pattern FT on 14K examples (LR 2e-6, 1 epoch, 27 seconds)
- **Data sources:** Qwen3.5-35B (95K), Grok 4.20 (29K), hand-crafted (235)
- **Key finding:** Full fine-tune dramatically outperforms LoRA for small models (+7% ROUGE-L on same data)

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "juanquivilla/sotto-cleanup-lfm25-350m",
    dtype=torch.bfloat16, device_map="auto", trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("juanquivilla/sotto-cleanup-lfm25-350m")

raw = "uh the server is uh running low on memory"
prompt = f"### Input:\n{raw}\n\n### Output:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
# → "The server is running low on memory."
```

## Quantized Variants

| Variant | Size | ROUGE-L | Filler-Free | Link |
|---------|------|---------|-------------|------|
| **bf16 (this model)** | 676MB | 0.931 | 90% | — |
| **MLX 5-bit (recommended)** | 233MB | 0.926 | 99% | [sotto-cleanup-lfm25-350m-mlx-5bit](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit) |
| MLX 4-bit | 190MB | 0.897 | 99% | [sotto-cleanup-lfm25-350m-mlx-4bit](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m-mlx-4bit) |

## Part of SottoASR

[**SottoASR**](https://sotto.app) is a local, privacy-first speech-to-text application for macOS. Press a hotkey, speak, and clean text appears at your cursor. All processing happens on-device — no audio or text is ever sent to a cloud service. This model powers the transcript cleanup step.

## License

This model inherits the [LFM 1.0 license](https://www.liquid.ai/license) from the base model.